Plant proteins are increasingly seen as critical nutrient sources for both amateur and professional athletes. The aim of the presented study was to review the inventions and experimental articles referring to the application of plant-based proteins, peptides and amino acids in food products dedicated to sportspeople and published in the period 2014-2023. The literature search was conducted according to PRISMA statementsacross several key databases, including Scopus and ISI Web of Science. Altogether, 106 patents and 35 original articles were found. The survey of patents and inventions described in the articles showed the use of 52 taxa (mainly annual herbaceous plants), creating edible seeds and representing mainly the families Fabaceae and Poaceae. The majority of inventions were developed by research teams numbering from two to five scientists, affiliated in China, The United States of America and Japan. The greatest number of inventions applied plant-based proteins (especially protein isolates), declared the nutritional activity and were prepared in liquid or solid consistency. According to the reviewed studies, the intake of soybean and potato proteins might provide better results than animal-based protein (excluding resistance training), whereas the consumption of pea and rice protein does not possess any unique anabolic properties over whey protein. The analysis of other investigations demonstrated the varied acceptability and consumption of food products, while the high rating of the tested food products presented in four articles seems to be an effect of their sensual values, as well as other elements, such as production method, health benefits and cost-effectiveness. Considering the great potential of useful plant species, it might be concluded that future investigations focusing on searching for novel plant protein sources, suitable for the preparation of food products dedicated to amateur and professional sportspeople, remain of interest.

BACKGROUND: The use of survey methodology in surgical research has proliferated in recent years, but the quality of these surveys and of their reporting is understudied.
METHODS: We conducted a comprehensive review of surgical survey literature (January 2022-July 2023) via PubMed in July 2023. Articles which (1) reported data gleaned from a survey, (2) were published in an English language journal, (3) targeted survey respondents in the United States or Canada, and (4) pertained to general surgery specialties were included. We assessed quality of survey reports using the Checklist for Reporting Of Survey Studies (CROSS) guidelines. Articles were evaluated for concordance with CROSS using a dichotomous (yes or no) scale.
RESULTS: Initial literature search yielded 481 articles; 57 articles were included in analysis based on the inclusion criteria. The mean response rate was 37% (range 0.62%-98%). The majority of surveys were administered electronically (n = 50, 87.8%). No publications adhered to all 40 CROSS items; on average, publications met 61.2% of items applicable to that study. Articles were most likely to adhere to reporting criteria for title and abstract (mean adherence 99.1%), introduction (99.1%), and discussion (92.4%). Articles were least adherent to items related to methodology (42.6%) and moderately adherent to items related to results (76.6%). Only five articles cited CROSS guidelines or another standardized survey reporting tool (10.5%).
CONCLUSIONS: Our analysis demonstrates that CROSS reporting guidelines for survey research have not been adopted widely. Surveys reported in surgical literature may be of variable quality. Increased adherence to guidelines could improve development and dissemination of surveys done by surgeons.

BACKGROUND: Current follow-up models in cancer are seen to be unsustainable and inflexible, and there is growing interest in alternative models, such as patient-initiated follow-up (PIFU). It is therefore important to understand whether PIFU is acceptable to patients and healthcare professionals (HCPs).
METHODS: Standard systematic review methodology aimed at limiting bias was used for study identification (to January 2022), selection and data extraction. Thematic synthesis was undertaken for qualitative data, and survey findings were tabulated and described.
RESULTS: Nine qualitative studies and 22 surveys were included, mainly in breast and endometrial cancer. Women treated for breast or endometrial cancer and HCPs were mostly supportive of PIFU. Facilitators for PIFU included convenience, control over own health and avoidance of anxiety-inducing clinic appointments. Barriers included loss of reassurance from scheduled visits and lack of confidence in self-management. HCPs were supportive of PIFU but concerned about resistance to change, unsuitability of PIFU for some patients and costs.
CONCLUSIONS: PIFU is viewed mostly positively by women treated for breast or endometrial cancer, and by HCPs, but further evidence is needed from a wider range of cancers, men, and more representative samples. A protocol was registered with PROSPERO (CRD42020181412).

The authors of the manuscript \'Complementary and alternative medicine - practice, attitudes, and knowledge among healthcare professionals in New Zealand: an integrative review\' [1] disagree with the assertion by McDowell et al. that our manuscript has extrapolation errors.

This letter is to highlight errors made by Liu et al. in their 2020 paper in BMC Complementary Medicine and Therapies, \"Complementary and alternative medicine-practice, attitudes, and knowledge among healthcare professionals in New Zealand: an integrative review\". Substantial errors in their citation of the recent research and methodology by McDowell, Kohut & Betts (2019) pertaining to the practice of acupuncture in New Zealand by physiotherapists are presented. The actual results of McDowell et al.\'s work and the true state of acupuncture use by their sample group is reported.

This is a scoping review on mapping the use of digital tools to assess food consumption in Brazil. Searches were carried out in nine electronic databases (Medline, Lilacs, Scopus, Embase, Web of Science, Science Direct, Ovid, Free Medical Journal and Crossref) to select studies published from October 2020 to December 2023. This review identified forty-eight digital tools in the 94 publications analyzed, the most frequent being web-based technologies (60%) and mobile devices (40%). Among these studies, 55% (n = 52) adopted a population-based approach, while 45% (n = 42) focused on specific regions. The predominant study design observed was cross-sectional (n = 63). A notable trend observed was the increasing frequency of validation studies in recent years. Although the use of digital tools in the assessment of food consumption in Brazil has grown in recent years, studies did not describe the process of creating and validating the tools, which would contribute to the improvement of data quality. Investments that allow the expansion of the use of the internet and mobile devices; the improvement of digital literacy; and the development of open-access tools, especially in the North and Northeast regions, are challenges that require a concerted effort towards providing equal opportunities, fostering encouragement, and delving deeper into the potential of digital tools within studies pertaining to food consumption in Brazil.

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients\' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients\' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists\' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

Air pollution is a primary concern, causing around 7 million premature deaths annually, with traffic-related sources contributing 23 %-45 % of emissions. While several studies have surveyed vehicle emission models, they are either outdated or focus on specific data-driven models. This paper systematically reviews vehicle emission prediction models, comparing traditional approaches with data-driven emission models. The traditional emission models can be divided into average-speed, modal, and other models, noting their reliance on empirical assumptions and parameters that may not be universally applicable. In contrast, we delve into data-driven models utilizing dynamometer and on-road test data for time-series and spatial-temporal predictions. The application of these models is discussed across various scenarios, highlighting the progress and gap. We observed that traditional models, primarily estimating total traffic emissions in study regions, lack micro-level detail crucial for tailored decisions. The direct link between road emission model accuracy and input data quality poses challenges in disaggregating on-road vehicle emission inventories. Due to unique transportation instruments, traffic fleet components, and patterns, exploring the effects of emission-reduction policies in specific cities or regions is urgent. Vehicle characteristics, environmental conditions, traffic scenarios, and prediction scales are common effect factors, while instantaneous driving profiles prove effective in model calibration. In data-driven models, ANN outperforms in estimating emissions and performance of low-power diesel engines with errors not exceeding 5 %. However, no single data-driven method performed excellently in predicting all pollutants. Besides, integrated methods utilizing LSTM, GRU, and RNN outperform individual models. To enhance prediction accuracy considering the inherent connectivity of road networks and spatiotemporal variation patterns of vehicle emissions, GCN is an emerging approach for capturing spatial-temporal relationships based on remote sensing data. Moreover, limited data-driven studies have been performed to forecast particle matter emissions, the main contributors to urban pollution, calling for more attention for future research.

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field\'s future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements.